Overview

Dataset statistics

Number of variables21
Number of observations1276
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory670.2 KiB
Average record size in memory537.9 B

Variable types

NUM15
CAT5
UNSUPPORTED1

Reproduction

Analysis started2021-02-27 20:32:15.741948
Analysis finished2021-02-27 20:32:49.741912
Duration34 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
title has a high cardinality: 1268 distinct values High cardinality
deltaMedianPrice is highly correlated with deltaAvgPriceHigh correlation
deltaAvgPrice is highly correlated with deltaMedianPriceHigh correlation
dublinNorthSouth is highly correlated with neighbourhoodHigh correlation
neighbourhood is highly correlated with dublinNorthSouthHigh correlation
title is uniformly distributed Uniform
df_index has unique values Unique
floorArea is an unsupported type, check if it needs cleaning or further analysis Unsupported
deltaMedianPrice has 46 (3.6%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

UNIQUE
Distinct count1276
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1473.0634796238244
Minimum0
Maximum2962
Zeros1
Zeros (%)0.1%
Memory size10.1 KiB
2021-02-27T20:32:49.832071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile208.75
Q1810.75
median1478.5
Q32122
95-th percentile2761.25
Maximum2962
Range2962
Interquartile range (IQR)1311.25

Descriptive statistics

Standard deviation795.7762134
Coefficient of variation (CV)0.5402185476
Kurtosis-1.058014595
Mean1473.06348
Median Absolute Deviation (MAD)653
Skewness0.03477867836
Sum1879629
Variance633259.7818
2021-02-27T20:32:49.957945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 1 0.1%
 
1324 1 0.1%
 
1339 1 0.1%
 
1338 1 0.1%
 
1335 1 0.1%
 
1332 1 0.1%
 
1331 1 0.1%
 
1326 1 0.1%
 
1325 1 0.1%
 
1323 1 0.1%
 
Other values (1266) 1266 99.2%
 
ValueCountFrequency (%) 
0 1 0.1%
 
9 1 0.1%
 
11 1 0.1%
 
14 1 0.1%
 
16 1 0.1%
 
ValueCountFrequency (%) 
2962 1 0.1%
 
2960 1 0.1%
 
2958 1 0.1%
 
2956 1 0.1%
 
2950 1 0.1%
 

title
Categorical

HIGH CARDINALITY
UNIFORM
Distinct count1268
Unique (%)99.4%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
20 Moyclare Road, Baldoyle, Dublin 13
 
2
24 The Wood, Millbrook Lawns, Tallaght, Dublin 24
 
2
Apartment 101, Milltown Hall, Milltown Avenue, Mount Saint Annes, Milltown, Dublin 6
 
2
26 Grace Park Court, Beaumont Road, Beaumont, Dublin 9
 
2
171 Drimnagh Road, Drimnagh, Dublin 12
 
2
Other values (1263)
1266
ValueCountFrequency (%) 
20 Moyclare Road, Baldoyle, Dublin 13 2 0.2%
 
24 The Wood, Millbrook Lawns, Tallaght, Dublin 24 2 0.2%
 
Apartment 101, Milltown Hall, Milltown Avenue, Mount Saint Annes, Milltown, Dublin 6 2 0.2%
 
26 Grace Park Court, Beaumont Road, Beaumont, Dublin 9 2 0.2%
 
171 Drimnagh Road, Drimnagh, Dublin 12 2 0.2%
 
Apartment 427, The Old Chocolate Factory, Kilmainham Square, Kilmainham, Dublin 8 2 0.2%
 
20 Muckross Green, Perrystown, Dublin 12 2 0.2%
 
88 Scholarstown Park, Rathfarnham, Dublin 16 2 0.2%
 
141 Collinswood, Collins Avenue, Beaumont, Dublin 9 1 0.1%
 
Apartment 84, The William Bligh, The Gasworks, Dublin 4 1 0.1%
 
Other values (1258) 1258 98.6%
 
2021-02-27T20:32:50.105051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length84
Mean length45.11755486
Min length22
ValueCountFrequency (%) 
Lowercase_Letter 27 38.0%
 
Uppercase_Letter 25 35.2%
 
Decimal_Number 10 14.1%
 
Other_Punctuation 4 5.6%
 
Dash_Punctuation 1 1.4%
 
Space_Separator 1 1.4%
 
Close_Punctuation 1 1.4%
 
Connector_Punctuation 1 1.4%
 
Open_Punctuation 1 1.4%
 
ValueCountFrequency (%) 
Latin 52 73.2%
 
Common 19 26.8%
 
ValueCountFrequency (%) 
ASCII 70 100.0%
 

neighbourhood
Categorical

HIGH CORRELATION
Distinct count22
Unique (%)1.7%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Dublin 15
 
123
Dublin 11
 
100
Dublin 8
 
97
Dublin 3
 
81
Dublin 9
 
80
Other values (17)
795
ValueCountFrequency (%) 
Dublin 15 123 9.6%
 
Dublin 11 100 7.8%
 
Dublin 8 97 7.6%
 
Dublin 3 81 6.3%
 
Dublin 9 80 6.3%
 
Dublin 4 77 6.0%
 
Dublin 14 73 5.7%
 
Dublin 24 71 5.6%
 
Dublin 7 71 5.6%
 
Dublin 12 67 5.3%
 
Other values (12) 436 34.2%
 
2021-02-27T20:32:50.241676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length9
Mean length8.536050157
Min length8
ValueCountFrequency (%) 
Decimal_Number 10 55.6%
 
Lowercase_Letter 5 27.8%
 
Uppercase_Letter 2 11.1%
 
Space_Separator 1 5.6%
 
ValueCountFrequency (%) 
Common 11 61.1%
 
Latin 7 38.9%
 
ValueCountFrequency (%) 
ASCII 18 100.0%
 

propertyType
Categorical

Distinct count10
Unique (%)0.8%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Apartment
395
Terrace
341
Semi-D
283
End of Terrace
113
Detached
 
67
Other values (5)
 
77
ValueCountFrequency (%) 
Apartment 395 31.0%
 
Terrace 341 26.7%
 
Semi-D 283 22.2%
 
End of Terrace 113 8.9%
 
Detached 67 5.3%
 
Duplex 32 2.5%
 
Bungalow 18 1.4%
 
Site 14 1.1%
 
Townhouse 12 0.9%
 
Studio 1 0.1%
 
2021-02-27T20:32:50.356883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length14
Mean length8.043887147
Min length4
ValueCountFrequency (%) 
Lowercase_Letter 19 70.4%
 
Uppercase_Letter 6 22.2%
 
Dash_Punctuation 1 3.7%
 
Space_Separator 1 3.7%
 
ValueCountFrequency (%) 
Latin 25 92.6%
 
Common 2 7.4%
 
ValueCountFrequency (%) 
ASCII 27 100.0%
 

numBedrooms
Real number (ℝ)

Distinct count9
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6120689655172415
Minimum-1.0
Maximum11.0
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:50.456215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum11
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.024780852
Coefficient of variation (CV)0.3923253426
Kurtosis5.042710271
Mean2.612068966
Median Absolute Deviation (MAD)1
Skewness0.4144378527
Sum3333
Variance1.050175794
2021-02-27T20:32:50.550148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3 518 40.6%
 
2 464 36.4%
 
4 131 10.3%
 
1 103 8.1%
 
5 38 3.0%
 
-1 15 1.2%
 
6 4 0.3%
 
7 2 0.2%
 
11 1 0.1%
 
ValueCountFrequency (%) 
-1 15 1.2%
 
1 103 8.1%
 
2 464 36.4%
 
3 518 40.6%
 
4 131 10.3%
 
ValueCountFrequency (%) 
11 1 0.1%
 
7 2 0.2%
 
6 4 0.3%
 
5 38 3.0%
 
4 131 10.3%
 

numBathrooms
Real number (ℝ)

Distinct count7
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6316614420062696
Minimum-1.0
Maximum6.0
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:50.654356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum6
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8535420445
Coefficient of variation (CV)0.5231122232
Kurtosis2.260186109
Mean1.631661442
Median Absolute Deviation (MAD)1
Skewness0.7076769502
Sum2082
Variance0.7285340218
2021-02-27T20:32:50.765372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 629 49.3%
 
2 454 35.6%
 
3 146 11.4%
 
4 22 1.7%
 
-1 18 1.4%
 
5 5 0.4%
 
6 2 0.2%
 
ValueCountFrequency (%) 
-1 18 1.4%
 
1 629 49.3%
 
2 454 35.6%
 
3 146 11.4%
 
4 22 1.7%
 
ValueCountFrequency (%) 
6 2 0.2%
 
5 5 0.4%
 
4 22 1.7%
 
3 146 11.4%
 
2 454 35.6%
 

floorArea
Unsupported

REJECTED
UNSUPPORTED
Missing0
Missing (%)0.0%
Memory size10.1 KiB

price
Real number (ℝ≥0)

Distinct count188
Unique (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396152.03761755483
Minimum75000.0
Maximum2500000.0
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:50.864886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile199000
Q1260000
median349000
Q3450000
95-th percentile795000
Maximum2500000
Range2425000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation220609.2038
Coefficient of variation (CV)0.556880144
Kurtosis21.38536286
Mean396152.0376
Median Absolute Deviation (MAD)94000
Skewness3.425419534
Sum505490000
Variance4.866842079e+10
2021-02-27T20:32:50.955298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
275000 43 3.4%
 
350000 40 3.1%
 
250000 35 2.7%
 
375000 34 2.7%
 
395000 33 2.6%
 
450000 31 2.4%
 
295000 31 2.4%
 
425000 30 2.4%
 
325000 29 2.3%
 
260000 29 2.3%
 
Other values (178) 941 73.7%
 
ValueCountFrequency (%) 
75000 1 0.1%
 
95000 1 0.1%
 
135000 2 0.2%
 
139000 1 0.1%
 
140000 3 0.2%
 
ValueCountFrequency (%) 
2500000 2 0.2%
 
2300000 1 0.1%
 
1800000 1 0.1%
 
1700000 1 0.1%
 
1600000 2 0.2%
 

rating
Categorical

Distinct count16
Unique (%)1.3%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
D2
168
D1
157
C3
129
C2
122
C1
 
107
Other values (11)
593
ValueCountFrequency (%) 
D2 168 13.2%
 
D1 157 12.3%
 
C3 129 10.1%
 
C2 122 9.6%
 
C1 107 8.4%
 
E1 103 8.1%
 
E2 92 7.2%
 
B3 90 7.1%
 
F 76 6.0%
 
G 67 5.3%
 
Other values (6) 165 12.9%
 
2021-02-27T20:32:51.089872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length6
Mean length2.079937304
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 10 66.7%
 
Decimal_Number 4 26.7%
 
Connector_Punctuation 1 6.7%
 
ValueCountFrequency (%) 
Latin 10 66.7%
 
Common 5 33.3%
 
ValueCountFrequency (%) 
ASCII 15 100.0%
 

sellerId
Real number (ℝ≥0)

Distinct count191
Unique (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5651.622257053292
Minimum7
Maximum11902
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:51.199530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q11367
median6498
Q39737
95-th percentile11062
Maximum11902
Range11895
Interquartile range (IQR)8370

Descriptive statistics

Standard deviation4231.153412
Coefficient of variation (CV)0.7486617504
Kurtosis-1.666174779
Mean5651.622257
Median Absolute Deviation (MAD)4441
Skewness-0.04532335691
Sum7211470
Variance17902659.19
2021-02-27T20:32:51.289438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11062 46 3.6%
 
10947 43 3.4%
 
11 40 3.1%
 
7569 35 2.7%
 
1413 33 2.6%
 
12 31 2.4%
 
8505 31 2.4%
 
6498 25 2.0%
 
9172 24 1.9%
 
10949 23 1.8%
 
Other values (181) 945 74.1%
 
ValueCountFrequency (%) 
7 6 0.5%
 
11 40 3.1%
 
12 31 2.4%
 
49 14 1.1%
 
56 8 0.6%
 
ValueCountFrequency (%) 
11902 4 0.3%
 
11766 6 0.5%
 
11754 1 0.1%
 
11720 2 0.2%
 
11635 1 0.1%
 

longitude
Real number (ℝ)

Distinct count1242
Unique (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.280365709914858
Minimum-6.443882
Maximum-6.055211
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:51.383452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.443882
5-th percentile-6.40275325
Q1-6.32030875
median-6.2762445
Q3-6.235324
95-th percentile-6.16926
Maximum-6.055211
Range0.388671
Interquartile range (IQR)0.08498475

Descriptive statistics

Standard deviation0.07048235548
Coefficient of variation (CV)-0.01122265147
Kurtosis-0.009692856351
Mean-6.28036571
Median Absolute Deviation (MAD)0.0417385
Skewness-0.07827101505
Sum-8013.746646
Variance0.004967762435
2021-02-27T20:32:51.491879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-6.227282 3 0.2%
 
-6.309374 3 0.2%
 
-6.249849 2 0.2%
 
-6.255195 2 0.2%
 
-6.25762 2 0.2%
 
-6.146247 2 0.2%
 
-6.275658 2 0.2%
 
-6.27907 2 0.2%
 
-6.313029 2 0.2%
 
-6.392492 2 0.2%
 
Other values (1232) 1254 98.3%
 
ValueCountFrequency (%) 
-6.443882 1 0.1%
 
-6.443585 1 0.1%
 
-6.442693 1 0.1%
 
-6.441123 1 0.1%
 
-6.440754 1 0.1%
 
ValueCountFrequency (%) 
-6.055211 1 0.1%
 
-6.059735 1 0.1%
 
-6.060574 1 0.1%
 
-6.065742 1 0.1%
 
-6.066756 1 0.1%
 

latitude
Real number (ℝ≥0)

Distinct count1241
Unique (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.3437929335963
Minimum53.21904
Maximum53.433172
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:51.613377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum53.21904
5-th percentile53.27083575
Q153.31776925
median53.34541
Q353.38014125
95-th percentile53.400625
Maximum53.433172
Range0.214132
Interquartile range (IQR)0.062372

Descriptive statistics

Standard deviation0.04162785884
Coefficient of variation (CV)0.0007803693092
Kurtosis-0.6607437934
Mean53.34379293
Median Absolute Deviation (MAD)0.0315843
Skewness-0.4028886929
Sum68066.67978
Variance0.001732878632
2021-02-27T20:32:51.700006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
53.342931 3 0.2%
 
53.341431 3 0.2%
 
53.332367 2 0.2%
 
53.33827 2 0.2%
 
53.313725 2 0.2%
 
53.314145 2 0.2%
 
53.337848 2 0.2%
 
53.26935 2 0.2%
 
53.349442 2 0.2%
 
53.247731 2 0.2%
 
Other values (1231) 1254 98.3%
 
ValueCountFrequency (%) 
53.21904 1 0.1%
 
53.226358 1 0.1%
 
53.228438 1 0.1%
 
53.229695 1 0.1%
 
53.233244 1 0.1%
 
ValueCountFrequency (%) 
53.433172 1 0.1%
 
53.432174 1 0.1%
 
53.431643 1 0.1%
 
53.422947 1 0.1%
 
53.422198 1 0.1%
 

pricePerBedroom
Real number (ℝ)

Distinct count298
Unique (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148095.4246902523
Minimum-1500000.0
Maximum625000.0
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:51.786052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1500000
5-th percentile78333.33333
Q1108333.3333
median137916.6667
Q3185625
95-th percentile275000
Maximum625000
Range2125000
Interquartile range (IQR)77291.66667

Descriptive statistics

Standard deviation100595.9864
Coefficient of variation (CV)0.6792646469
Kurtosis79.13782629
Mean148095.4247
Median Absolute Deviation (MAD)37083.33333
Skewness-5.543220326
Sum188969761.9
Variance1.011955247e+10
2021-02-27T20:32:51.876887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
125000 36 2.8%
 
150000 31 2.4%
 
175000 29 2.3%
 
137500 23 1.8%
 
187500 21 1.6%
 
165000 20 1.6%
 
100000 18 1.4%
 
95000 18 1.4%
 
162500 18 1.4%
 
112500 18 1.4%
 
Other values (288) 1044 81.8%
 
ValueCountFrequency (%) 
-1500000 1 0.1%
 
-850000 2 0.2%
 
-745000 1 0.1%
 
-498000 1 0.1%
 
-350000 1 0.1%
 
ValueCountFrequency (%) 
625000 2 0.2%
 
600000 1 0.1%
 
497500 1 0.1%
 
460000 1 0.1%
 
450000 2 0.2%
 

deltaAvgPrice
Real number (ℝ)

HIGH CORRELATION
Distinct count798
Unique (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20002.763963719077
Minimum-1785392.8571428573
Maximum484607.14285714284
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:51.979506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1785392.857
5-th percentile-276689.4323
Q1-43461.5957
median45311.11806
Q3108650.7878
95-th percentile274207.0183
Maximum484607.1429
Range2270000
Interquartile range (IQR)152112.3835

Descriptive statistics

Standard deviation192861.8567
Coefficient of variation (CV)9.641760363
Kurtosis19.83974212
Mean20002.76396
Median Absolute Deviation (MAD)74549.70238
Skewness-2.881062237
Sum25523526.82
Variance3.719569579e+10
2021-02-27T20:32:52.080679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-20509.09091 8 0.6%
 
92284.61538 7 0.5%
 
63767.85714 7 0.5%
 
339607.1429 6 0.5%
 
-82715.38462 6 0.5%
 
67284.61538 6 0.5%
 
83567.01031 6 0.5%
 
59252.52525 6 0.5%
 
103767.8571 6 0.5%
 
24886.95652 6 0.5%
 
Other values (788) 1212 95.0%
 
ValueCountFrequency (%) 
-1785392.857 2 0.2%
 
-1585392.857 1 0.1%
 
-1329304.762 1 0.1%
 
-1066482.456 1 0.1%
 
-991812.5 1 0.1%
 
ValueCountFrequency (%) 
484607.1429 1 0.1%
 
464607.1429 1 0.1%
 
434517.5439 1 0.1%
 
424607.1429 1 0.1%
 
423517.5439 1 0.1%
 

deltaMedianPrice
Real number (ℝ)

HIGH CORRELATION
ZEROS
Distinct count310
Unique (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-27913.401253918495
Minimum-1972500.0
Maximum345000.0
Zeros46
Zeros (%)3.6%
Memory size10.1 KiB
2021-02-27T20:32:52.183147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1972500
5-th percentile-356250
Q1-71000
median5000
Q365000
95-th percentile179250
Maximum345000
Range2317500
Interquartile range (IQR)136000

Descriptive statistics

Standard deviation193840.6495
Coefficient of variation (CV)-6.944357935
Kurtosis26.01250583
Mean-27913.40125
Median Absolute Deviation (MAD)67000
Skewness-3.675974581
Sum-35617500
Variance3.75741974e+10
2021-02-27T20:32:52.267841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 46 3.6%
 
25000 26 2.0%
 
45000 25 2.0%
 
60000 25 2.0%
 
-10000 24 1.9%
 
50000 24 1.9%
 
75000 22 1.7%
 
40000 22 1.7%
 
20000 22 1.7%
 
15000 21 1.6%
 
Other values (300) 1019 79.9%
 
ValueCountFrequency (%) 
-1972500 2 0.2%
 
-1772500 1 0.1%
 
-1405000 1 0.1%
 
-1175000 1 0.1%
 
-1072500 2 0.2%
 
ValueCountFrequency (%) 
345000 1 0.1%
 
326000 1 0.1%
 
315000 2 0.2%
 
305000 1 0.1%
 
297500 1 0.1%
 

dublinNorthSouth
Categorical

HIGH CORRELATION
Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
S
666
N
610
ValueCountFrequency (%) 
S 666 52.2%
 
N 610 47.8%
 
2021-02-27T20:32:52.374023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length1
Mean length1
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 2 100.0%
 
ValueCountFrequency (%) 
Latin 2 100.0%
 
ValueCountFrequency (%) 
ASCII 2 100.0%
 

distToCity
Real number (ℝ≥0)

Distinct count1245
Unique (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8688657073764485
Minimum0.09991156306907832
Maximum17.012878032749462
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:52.462528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.09991156307
5-th percentile1.377932311
Q13.028898595
median5.082270001
Q38.621259288
95-th percentile12.09076667
Maximum17.01287803
Range16.91296647
Interquartile range (IQR)5.592360693

Descriptive statistics

Standard deviation3.472494848
Coefficient of variation (CV)0.5916807473
Kurtosis-0.5953562912
Mean5.868865707
Median Absolute Deviation (MAD)2.571887899
Skewness0.5388872222
Sum7488.672643
Variance12.05822047
2021-02-27T20:32:52.553846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.590435795 3 0.2%
 
2.415355904 3 0.2%
 
8.940731434 2 0.2%
 
8.340624342 2 0.2%
 
8.438129316 2 0.2%
 
10.88445414 2 0.2%
 
1.463954511 2 0.2%
 
1.841780665 2 0.2%
 
6.879802013 2 0.2%
 
1.65164596 2 0.2%
 
Other values (1235) 1254 98.3%
 
ValueCountFrequency (%) 
0.09991156307 1 0.1%
 
0.1835478493 1 0.1%
 
0.2065863985 2 0.2%
 
0.3172734398 1 0.1%
 
0.3828696698 1 0.1%
 
ValueCountFrequency (%) 
17.01287803 1 0.1%
 
16.56325022 1 0.1%
 
16.47248576 1 0.1%
 
16.24328956 1 0.1%
 
15.94972834 1 0.1%
 

daysSincePublished
Real number (ℝ≥0)

Distinct count155
Unique (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.42398119122257
Minimum1
Maximum339
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:52.643307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.75
Q144
median82
Q3108
95-th percentile144
Maximum339
Range338
Interquartile range (IQR)64

Descriptive statistics

Standard deviation43.61229927
Coefficient of variation (CV)0.5491074436
Kurtosis1.980859241
Mean79.42398119
Median Absolute Deviation (MAD)30
Skewness0.5548587998
Sum101345
Variance1902.032648
2021-02-27T20:32:52.755379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
54 35 2.7%
 
74 34 2.7%
 
103 33 2.6%
 
87 29 2.3%
 
115 25 2.0%
 
106 25 2.0%
 
94 24 1.9%
 
38 24 1.9%
 
114 23 1.8%
 
67 23 1.8%
 
Other values (145) 1001 78.4%
 
ValueCountFrequency (%) 
1 4 0.3%
 
2 8 0.6%
 
3 13 1.0%
 
4 6 0.5%
 
5 10 0.8%
 
ValueCountFrequency (%) 
339 1 0.1%
 
336 1 0.1%
 
303 1 0.1%
 
229 1 0.1%
 
228 1 0.1%
 

numFood
Real number (ℝ≥0)

Distinct count46
Unique (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.405956112852664
Minimum11
Maximum60
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:52.865680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile30
Q142
median49
Q352
95-th percentile57
Maximum60
Range49
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.254563312
Coefficient of variation (CV)0.1778772383
Kurtosis1.403756982
Mean46.40595611
Median Absolute Deviation (MAD)4
Skewness-1.14037135
Sum59214
Variance68.13781548
2021-02-27T20:32:52.958689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
49 105 8.2%
 
50 98 7.7%
 
52 86 6.7%
 
48 78 6.1%
 
53 74 5.8%
 
51 68 5.3%
 
54 67 5.3%
 
47 57 4.5%
 
43 48 3.8%
 
46 46 3.6%
 
Other values (36) 549 43.0%
 
ValueCountFrequency (%) 
11 2 0.2%
 
13 1 0.1%
 
15 2 0.2%
 
16 3 0.2%
 
19 2 0.2%
 
ValueCountFrequency (%) 
60 5 0.4%
 
59 16 1.3%
 
58 26 2.0%
 
57 30 2.4%
 
56 32 2.5%
 

numRecreation
Real number (ℝ≥0)

Distinct count23
Unique (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.52115987460815
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:53.050169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median13
Q316
95-th percentile18.25
Maximum23
Range22
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.990029602
Coefficient of variation (CV)0.3186629387
Kurtosis-0.3872828611
Mean12.52115987
Median Absolute Deviation (MAD)3
Skewness-0.3412107627
Sum15977
Variance15.92033622
2021-02-27T20:32:53.132541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12 165 12.9%
 
13 130 10.2%
 
15 121 9.5%
 
17 105 8.2%
 
16 97 7.6%
 
9 92 7.2%
 
11 90 7.1%
 
14 89 7.0%
 
10 66 5.2%
 
7 58 4.5%
 
Other values (13) 263 20.6%
 
ValueCountFrequency (%) 
1 2 0.2%
 
2 2 0.2%
 
3 14 1.1%
 
4 21 1.6%
 
5 47 3.7%
 
ValueCountFrequency (%) 
23 1 0.1%
 
22 1 0.1%
 
21 3 0.2%
 
20 21 1.6%
 
19 38 3.0%
 

numShop
Real number (ℝ≥0)

Distinct count37
Unique (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.244514106583072
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-02-27T20:32:53.224732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median7
Q318
95-th percentile28.25
Maximum37
Range36
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.636889037
Coefficient of variation (CV)0.7680980214
Kurtosis-0.1124810606
Mean11.24451411
Median Absolute Deviation (MAD)3
Skewness1.014075061
Sum14348
Variance74.59585223
2021-02-27T20:32:53.345814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4 278 21.8%
 
5 176 13.8%
 
6 77 6.0%
 
7 58 4.5%
 
8 50 3.9%
 
9 41 3.2%
 
22 38 3.0%
 
11 37 2.9%
 
10 37 2.9%
 
20 36 2.8%
 
Other values (27) 448 35.1%
 
ValueCountFrequency (%) 
1 8 0.6%
 
2 34 2.7%
 
3 23 1.8%
 
4 278 21.8%
 
5 176 13.8%
 
ValueCountFrequency (%) 
37 1 0.1%
 
36 1 0.1%
 
35 10 0.8%
 
34 5 0.4%
 
33 10 0.8%
 

Interactions

2021-02-27T20:32:20.256222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:20.402138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:20.530118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:20.681965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:20.819083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:20.957337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:21.108403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:21.365767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:21.539276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:21.679675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:21.830812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:21.987498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:22.136442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:22.273656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:22.391679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:22.514843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:22.647466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:22.783278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:22.929926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.052876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.169762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.298970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.408883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.524049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.654078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.780993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:23.901393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:24.024423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:24.136434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:24.261771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:24.440049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:24.669039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:24.809415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:24.949409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:25.126608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:25.323190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:25.471904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:25.582836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:25.708627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:25.849230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:25.976359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:26.123004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:26.260403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:26.526326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:26.681554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:26.818248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:26.934231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.065152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.201849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.317571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.435735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.582735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.694511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.819988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:27.945113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.066992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.184584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.299891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.417226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.545549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.667163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.788017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:28.901175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.017645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.138963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.265787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.393577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.500038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.611070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.718546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.828835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:29.943362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.059658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.167716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.274120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.389693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.513056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.633450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.757392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.877523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:30.999589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:31.131274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:31.249820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:31.371644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:31.491389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:31.610357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:31.739392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.053806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.182803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.300982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.425820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.527007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.625889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.729505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.831005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:32.933302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.043117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.138183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.237617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.336729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.435277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.542510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.648402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.747425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.844315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:33.949300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.056075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.160875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.268544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.372588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.480470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.601106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.704864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.811148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:34.915514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.019022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.129592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.240587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.343999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.444631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.553534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.664147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.771070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.879176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:35.983546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.091114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.206148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.316214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.428334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.538168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.654084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.767300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.877719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:36.982236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:37.082608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:37.195447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:37.302940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:37.405837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:37.514353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-27T20:32:37.731078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:37.845333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:37.945830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:38.049313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:38.152116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:38.481762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:38.603962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:38.730147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:38.847601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:38.949933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:39.074055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:39.221920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:39.359403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:39.506836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:39.643656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:39.785525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:39.934407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:40.061110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:40.199180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:40.344953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:40.467640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:40.614656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:40.758914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:40.886397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.003312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.132035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.250644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.382818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.517940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.656450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.803522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:41.945899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:42.062280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:42.201245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:42.331909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:42.450974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:42.590926image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:42.737149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:42.873200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.009079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.158017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.271693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.379608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.509092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.639997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.755198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:43.880506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:44.009351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:44.132466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:44.241694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:44.354444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:44.492680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-27T20:32:44.845500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:44.979378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:45.096003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-27T20:32:45.685591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:45.787443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:45.898501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-27T20:32:46.370608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:46.480145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:46.592875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:46.727881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:46.870473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:46.996451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:47.340368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:47.470908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:47.608054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:47.733796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:47.850910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:47.972918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:48.092617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:48.225547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:48.354031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:48.498329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:48.637645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:48.765008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-27T20:32:53.502936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-27T20:32:53.763801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-27T20:32:53.995039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-27T20:32:54.233382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-27T20:32:54.478734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-27T20:32:49.066249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-27T20:32:49.558524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
001 Moatfield Park, Coolock, Artane, Dublin 5Dublin 5Semi-D3.01.091380000.0E2453-6.19311753.388871126666.66666714794.642857-5000.0N5.8571725491712
19150 Broadford Rise, Ballinteer, Dublin 16Dublin 16Semi-D3.02.0102495000.0C349-6.26110853.277830165000.00000013187.500000-20000.0S8.366932347915
211Apartment 172, Block C, Dublin 7Dublin 7Apartment1.01.051250000.0C11331-6.27758453.348715250000.000000128567.010309100000.0N1.387431953124
31426 Manorfields Walk, Castaheany, Clonee, Dublin 15Dublin 15Terrace2.01.070250000.0D11186-6.42442853.397586125000.00000088767.85714330000.0N12.090038846825
416Ayla, 39 Beach Road, Sandymount, Dublin 4Dublin 4Terrace3.04.0147950000.0C1893-6.21588353.335437316666.666667-235392.857143-422500.0S3.414646848194
541Apartment 156, Block 6, Harcourt Green, Dublin 2Dublin 2Apartment2.02.075399000.0B3262-6.26018553.331722199500.00000016935.483871-4000.0S2.380052251164
643Apt 9 Lucerne, 39 Castle Avenue, Clontarf, Dublin 3Dublin 3Apartment2.01.055310000.0SI_66611766-6.20708653.362925155000.000000160695.23809585000.0N3.548787842207
75046 Oxmantown Road, Stoneybatter, Dublin 7Dublin 7Terrace2.0-1.056280000.0F1087-6.29165453.353738140000.00000098567.01030970000.0N2.2333301553124
85471 Parklands Court, Ballycullen, Dublin 16Dublin 16Apartment2.01.070255000.0ZZZ8210-6.34142853.274936127500.000000253187.500000220000.0S10.3016421528923
95524 The Wood, Millbrook Lawns, Tallaght, Dublin 24Dublin 24Terrace3.01.090285000.0ZZZ8210-6.35503353.28175995000.000000-2389.473684-10000.0S10.2147841538731

Last rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
126629245 Dun Emer Drive, Dundrum, Dublin 14Dublin 14Semi-D4.01.0138645000.0D249-6.23697653.282698161250.000000-49691.489362-55000.0S7.946910113481012
12672928133 Kiltipper Gate, Tallaght, Dublin 24Dublin 24Apartment2.02.070215000.0ZZZ8210-6.37067253.269495107500.00000067610.52631660000.0S11.9279891522520
1268293019 Beneavin Park, Glasnevin, Dublin 11Dublin 11Semi-D3.01.097370000.0D2841-6.28328953.389930123333.333333-68213.675214-95000.0N4.4228599551184
126929352 Florence Street, Portobello, Dublin 8Dublin 8Terrace3.02.0124595000.0E28456-6.26972353.331242198333.333333-252715.384615-300000.0S2.5504578954154
127029399 Saint Johns Court, Kilmore Road, Artane, Dublin 5Dublin 5Terrace2.01.071285000.0C31063-6.21893153.390851142500.000000109794.64285790000.0N4.9303232456138
12712950Apt 4, Slane House, Patrick Street, Christchurch, Dublin 8Dublin 8Apartment1.01.037230000.0E26223-6.27255253.340047230000.000000112284.61538565000.0S1.742339256114
127229564 Old Mount Pleasant, Ranelagh, Dublin 6Dublin 6Terrace3.01.0154825000.0SI_666262-6.25815853.326563275000.000000-191482.456140-300000.0S2.948946352165
12732958122 Connaught Street, Phibsborough, Dublin 7Dublin 7Terrace3.01.0100550000.0E2841-6.27934553.363448183333.333333-171432.989691-200000.0N1.82382712057124
1274296010 Ard Na Greine, Eaton Brae, off Orwell Road, Rathgar, Dublin 6Dublin 6Terrace2.02.0144900000.0A39460-6.26159553.303673450000.000000-266482.456140-375000.0S5.4977944746189
127529623 Marian Drive, Rathfarnham, Dublin 14Dublin 14Detached5.03.0214850000.0C111062-6.29615753.295243170000.000000-254691.489362-260000.0S6.91020126501011